PROJECT SUMMARY/ABSTRACT This proposed study is motivated by the public health importance of aortic stenosis (AS), a common form of valvular heart disease that is associated with substantial morbidity and mortality. Once symptomatic, survival is a dismal 50% at 1-year if left untreated. Clinical symptoms develop once cardiac compensatory mechanisms fail, indicating the need for aortic valve replacement (AVR), but because AS primarily affects the elderly, symptoms are often incorrectly attributed to comorbid conditions, advanced age, or both?resulting in delayed treatment. While most patients that eventually undergo AVR experience improvements in symptoms and survival, nearly half of patients die within the first year after AVR or fail to reap the symptom and health status improvements for which they underwent AVR. Our research suggests that irreversible cardiac remodeling and injury related to delayed treatment contribute to these poor clinical outcomes, highlighting the unmet need for objective and sensitive measures to inform clinical decisions regarding the timing of AVR. Our long-term goal is to develop and implement an omics-based precision medicine approach for identifying patients with severe AS at-risk for irreversible cardiac remodeling and injury who would benefit from earlier AVR, and our central hypothesis is that multi-omic signatures reflective of dimensions of cardiac structure and function will identify irreversible cardiac remodeling and therefore predict the clinical response to AVR. Our prior work and preliminary studies provide strong support for our hypothesis and demonstrate that our experienced multidisciplinary team is uniquely qualified to complete the proposed study. We have identified metabolomic signatures that relate strongly to measures of cardiac function and structure and predict mortality after AVR, and also proteomic signatures relating to cardiac function that differentiate severe AS and associate with mortality. Within this proposal, we adopt a longitudinal systems biology approach that leverages the latest in proteomic and metabolomic (multi-omic) sciences to: discover, test, and cross-validate multi-omic signatures of cardiac function and structure in patients with severe AS (Aim 1); characterize longitudinal multi-omic signatures and associations with changes in cardiac structure and function after AVR (Aim 2); and evaluate the accuracy with which multi-omic signatures predict response to AVR (Aim 3). Our approach will enable us to identify multi-omic signatures of irreversible cardiac remodeling and injury in patients with severe AS. These data will support the development of new precision medicine-based strategies for identifying patients who would benefit from earlier clinical intervention in an effort to reduce mortality and maximize health after AVR.